richardcsuwandi/cake
[NeurIPS 2025] Context-Aware Kernel Evolution (CAKE)
This tool helps researchers and engineers who use Bayesian optimization to efficiently find optimal settings for experiments or models. It automates the complex task of selecting the best mathematical 'kernel' for their optimization process. By taking in initial experimental data, it uses AI to evolve and output the most suitable kernel, significantly reducing manual effort and specialized expertise.
No commits in the last 6 months.
Use this if you are performing Bayesian optimization and struggle with manually selecting or designing the right kernel function to effectively explore and exploit your optimization landscape.
Not ideal if you are looking for a general-purpose optimization library that doesn't focus specifically on Gaussian Process kernel selection for Bayesian optimization.
Stars
21
Forks
—
Language
Python
License
MIT
Category
Last pushed
Oct 13, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/llm-tools/richardcsuwandi/cake"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
MMMU-Benchmark/MMMU
This repo contains evaluation code for the paper "MMMU: A Massive Multi-discipline Multimodal...
pat-jj/DeepRetrieval
[COLM’25] DeepRetrieval — 🔥 Training Search Agent by RLVR with Retrieval Outcome
lupantech/MathVista
MathVista: data, code, and evaluation for Mathematical Reasoning in Visual Contexts
x66ccff/liveideabench
[𝐍𝐚𝐭𝐮𝐫𝐞 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬] 🤖💡 LiveIdeaBench: Evaluating LLMs' Scientific Creativity and Idea...
ise-uiuc/magicoder
[ICML'24] Magicoder: Empowering Code Generation with OSS-Instruct